Background: The VA has committed extensive resources to ensure that timely screening colonoscopy is readily available for eligible Veterans. To meet Veteran screening demands, the VA also contracts with outside fee-for service colonoscopy providers - a policy that costs over $600 per outsourced patient. However, a more cost-effective approach might be to maximize existing VA capacity by addressing patient absenteeism - a prevalent and expensive problem in VA gastrointestinal (GI) endoscopy units. This clinical research aims to make the most of existing VA resources through a scheduling approach called no-show predictive overbooking - a strategy that borrows from the airline industry practice of flight overbooking, coupled with the queuing theory employed at theme parks like Disneyland. In a 45-day retrospective pilot trial in our own GI unit at the West Los Angeles VA (WLAVA), we found that using no-show predictive overbooking would increase median utilization from 78% to 90% of capacity, increase the number of Veterans served by 21%, and save $14,805 ($329 per day) from avoiding fee-for-service outsourcing. Moreover, unlike with the airline industry, this strategy improved efficiency without bumping patients because of unexpected service denials. Aim: To perform a pragmatic clinical trial in the WLAVA GI unit to compare outcomes of traditional one patient, one slot scheduling vs. no-show predictive overbooking, including differences in percent utilization of capacity, number of Veterans served, mean lag time between scheduling and procedure performance, direct costs of care, and number of unexpected service denials (bumps) from no-show predictive overbooking Methods: The no-show overbooking intervention employs a logistic regression model that uses patient data to predict the odds of no-showing with 80% accuracy. The model runs automatically using a tailored software program that extracts and analyzes data from VISTA to automatically calculate daily predicted no-show rates; these results are compared to the known capacity of the GI unit. Assisted by an electronic scheduling grid showing projected appointment vacancies, clerks overbook patients who agree to join a fast track short-call line. At the time of GI scheduling, patients undergoing upper endoscopy (a procedure that requires no preparation) are offered 2 options: (1) to join the usual scheduling line, in which all patients are guaranteed to be seen within 30-60 days; or (2) to join the fast track line, in which patients are overbooked earlier into projected schedule openings. However, patients in the fast track assume a small risk of service denial on the day of their overbooking in case of inaccurate predictions. If this occurs, the patient is guaranteed service in the next available position and is assured of having a shorter wait time (<14 days) compared to those booked by usual scheduling. By rapidly processing upper endoscopy patients and moving them out of traditional slots, we predict that more scheduling slots would become available for patients awaiting colonoscopy. We propose to conduct a prospective, 24-month, interrupted time series (ITS) trial in the WLAVA GI clinic and endoscopy unit. The ITS design calls for alternating 4-month intervention periods (no-show predictive overbooking) with 4-month control periods (one patient, one slot scheduling). During control periods, scheduling clerks will allocate cases according to usual practice. During intervention periods, we will activate the no-show predictive overbooking strategy described above. We will compare outcomes between scheduling strategies, including differences in percent utilization of capacity (primary outcome), number of Veterans served, mean patient lag time between scheduling and procedure, number of unexpected service denials (bumps) from no-show predictive overbooking, and direct costs of care. We will analyze differences using both traditional univariate and multivariate approaches, and using autoregressive integrated moving average (ARIMA) analyses to adjust for auto-correlations in ITS data. We believe that if this approach were successful, then it may also serve as a scheduling model for other VA resources beyond GI units.